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Imitation learning holds the promise of equipping robots with versatile skills by learning from expert demonstrations.
Emergence of invariance and disentanglement in deep representations
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Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
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Dynamics generalization via information bottleneck in deep reinforcement learning
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Zeke Xie, Issei Sato, and Masashi Sugiyama · 2020
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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π 0 \pi_{0} : A vision-language-action flow model for general robot control, 2024
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Droid: A large-scale in-the-wild robot manipulation dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, et al · 2024
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Data scaling laws in imitation learning for robotic manipulation
Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, and Yang Gao · 2024
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Octo: An open-source generalist robot policy
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, et al · 2024
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Gr00t n1: An open foundation model for generalist humanoid robots
Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al · 2025
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Correlation information bottleneck: Towards adapting pretrained multimodal models for robust visual question answering
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Otter: A vision-language-action model with text-aware visual feature extraction
Huang Huang, Fangchen Liu, Letian Fu, Tingfan Wu, Mustafa Mukadam, Jitendra Malik, Ken Goldberg, and Pieter Abbeel · 2025
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